Highly autonomous generative agents powered by large language models promise
to simulate intricate social behaviors in virtual societies. However, achieving
real-time interactions with humans at a low computational cost remains
challenging. Here, we introduce Lyfe Agents. They combine low-cost with
real-time responsiveness, all while remaining intelligent and goal-oriented.
Key innovations include: (1) an option-action framework, reducing the cost of
high-level decisions; (2) asynchronous self-monitoring for better
self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing
critical memory items at a low cost. We evaluate Lyfe Agents' self-motivation
and sociability across several multi-agent scenarios in our custom LyfeGame 3D
virtual environment platform. When equipped with our brain-inspired techniques,
Lyfe Agents can exhibit human-like self-motivated social reasoning. For
example, the agents can solve a crime (a murder mystery) through autonomous
collaboration and information exchange. Meanwhile, our techniques enabled Lyfe
Agents to operate at a computational cost 10-100 times lower than existing
alternatives. Our findings underscore the transformative potential of
autonomous generative agents to enrich human social experiences in virtual
worlds.

利用 Lyfe Agents 实现了高度自治生成智能代理，能够在虚拟社会中模拟复杂的社交行为，并能以低计算成本与人进行实时互动，通过自主协作和信息交流解决犯罪等问题，同时比现有替代方案的计算成本低 10-100 倍。